Week 14 Hiring.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2b9c7746-f8f6-4645-b163-0ff303352753",
   "metadata": {},
   "source": [
    "# Hiring\n",
    " A small linear regression project for hiring people and predicting people's salaries. Our dataset has both missing values and object data type, so we have to fill in the missing values and convert the object data type to numeric data type and prepare it for modeling. Because the data are all scales, there is no need to standardize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4972ba60",
   "metadata": {
    "executionInfo": {
     "elapsed": 493,
     "status": "ok",
     "timestamp": 1684599836932,
     "user": {
      "displayName": "Moones Fallah",
      "userId": "00329903373398097609"
     },
     "user_tz": -210
    },
    "id": "4972ba60"
   },
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1eb7c88d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "executionInfo": {
     "elapsed": 573,
     "status": "error",
     "timestamp": 1684599850592,
     "user": {
      "displayName": "Moones Fallah",
      "userId": "00329903373398097609"
     },
     "user_tz": -210
    },
    "id": "1eb7c88d",
    "outputId": "99f3fae8-35e5-47ea-9959-f4c87643b947"
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel('hiring.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0ce1d5fe",
   "metadata": {
    "id": "0ce1d5fe",
    "outputId": "e1cbdf8a-9acd-410d-ce94-9c4b2debffcb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experience</th>\n",
       "      <th>test_score</th>\n",
       "      <th>interview_score</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>five</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>seven</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>two</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>seven</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>three</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>ten</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>eleven</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>89000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   experience  test_score  interview_score  salary\n",
       "0         NaN         8.0                9   50000\n",
       "1         NaN         8.0                6   45000\n",
       "2        five         6.0                7   60000\n",
       "3         two        10.0               10   65000\n",
       "4       seven         9.0                6   70000\n",
       "..        ...         ...              ...     ...\n",
       "91        two         8.0               10   65000\n",
       "92      seven         9.0                6   70000\n",
       "93      three         7.0               10   62000\n",
       "94        ten         NaN                7   72000\n",
       "95     eleven         7.0                8   89000\n",
       "\n",
       "[96 rows x 4 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b5e157fd",
   "metadata": {
    "id": "b5e157fd",
    "outputId": "820fe4a2-7d5f-4a7e-d439-9c23f0ec8862"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 96 entries, 0 to 95\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   experience       72 non-null     object \n",
      " 1   test_score       84 non-null     float64\n",
      " 2   interview_score  96 non-null     int64  \n",
      " 3   salary           96 non-null     int64  \n",
      "dtypes: float64(1), int64(2), object(1)\n",
      "memory usage: 3.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "62b2c6bf-81b1-49b9-b485-ae0d043e1747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['experience'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c06200f5",
   "metadata": {
    "id": "c06200f5"
   },
   "outputs": [],
   "source": [
    "df['experience'].fillna(0, inplace=True) # we assume None experience is equal 0 experience and fill non with with 0\n",
    "\n",
    "df['test_score'].fillna(df['test_score'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1f106c96",
   "metadata": {
    "id": "1f106c96"
   },
   "outputs": [],
   "source": [
    "X=df.iloc[: , :3]\n",
    "y = df.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "34c7e127",
   "metadata": {
    "id": "34c7e127",
    "outputId": "9b58c6f8-5867-4178-ff2c-c1a11e8ea5d6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experience</th>\n",
       "      <th>test_score</th>\n",
       "      <th>interview_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>five</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>seven</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>two</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>seven</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>three</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>ten</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>eleven</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   experience  test_score  interview_score\n",
       "0           0         8.0                9\n",
       "1           0         8.0                6\n",
       "2        five         6.0                7\n",
       "3         two        10.0               10\n",
       "4       seven         9.0                6\n",
       "..        ...         ...              ...\n",
       "91        two         8.0               10\n",
       "92      seven         9.0                6\n",
       "93      three         7.0               10\n",
       "94        ten         7.5                7\n",
       "95     eleven         7.0                8\n",
       "\n",
       "[96 rows x 3 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c7a6e9be-3620-4463-b955-30f114cbc34d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     50000\n",
       "1     45000\n",
       "2     60000\n",
       "3     65000\n",
       "4     70000\n",
       "      ...  \n",
       "91    65000\n",
       "92    70000\n",
       "93    62000\n",
       "94    72000\n",
       "95    89000\n",
       "Name: salary, Length: 96, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7b2c3068",
   "metadata": {
    "id": "7b2c3068"
   },
   "outputs": [],
   "source": [
    "#Converting words to integer values\n",
    "def convert_to_int(word):\n",
    "    word_dict = {'one':1, 'two':2, 'three':3, 'four':4, 'five':5, 'six':6, 'seven':7, 'eight':8,\n",
    "                'nine':9, 'ten':10, 'eleven':11, 'twelve':12, 'zero':0, 0: 0}\n",
    "    return word_dict[word]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e7b3d0eb-a5b9-4711-bc96-3de43d01ffb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experience</th>\n",
       "      <th>test_score</th>\n",
       "      <th>interview_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>five</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>seven</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>two</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>seven</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>three</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>ten</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>eleven</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   experience  test_score  interview_score\n",
       "0           0         8.0                9\n",
       "1           0         8.0                6\n",
       "2        five         6.0                7\n",
       "3         two        10.0               10\n",
       "4       seven         9.0                6\n",
       "..        ...         ...              ...\n",
       "91        two         8.0               10\n",
       "92      seven         9.0                6\n",
       "93      three         7.0               10\n",
       "94        ten         7.5                7\n",
       "95     eleven         7.0                8\n",
       "\n",
       "[96 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5363667a",
   "metadata": {
    "id": "5363667a"
   },
   "outputs": [],
   "source": [
    "X['experience'] = X['experience'].apply(convert_to_int)  # apply the function to all rows of \"experience\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b3c0a5d2-c525-49a3-8737-7ae46c97ea30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experience</th>\n",
       "      <th>test_score</th>\n",
       "      <th>interview_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>10</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    experience  test_score  interview_score\n",
       "0            0         8.0                9\n",
       "1            0         8.0                6\n",
       "2            5         6.0                7\n",
       "3            2        10.0               10\n",
       "4            7         9.0                6\n",
       "..         ...         ...              ...\n",
       "91           2         8.0               10\n",
       "92           7         9.0                6\n",
       "93           3         7.0               10\n",
       "94          10         7.5                7\n",
       "95          11         7.0                8\n",
       "\n",
       "[96 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f29606f3",
   "metadata": {
    "id": "f29606f3"
   },
   "outputs": [],
   "source": [
    "#Splitting Training and Test Set`\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4086953e",
   "metadata": {
    "id": "4086953e",
    "outputId": "8b6d54f0-2ff0-4434-e974-bae982f9b043"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating and Training the Model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression()\n",
    "\n",
    "#Fitting model with trainig data\n",
    "lm.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "56e38302",
   "metadata": {
    "id": "56e38302"
   },
   "outputs": [],
   "source": [
    "#test the model\n",
    "predictions = lm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "46628c75",
   "metadata": {
    "id": "46628c75"
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "859c4579",
   "metadata": {
    "id": "859c4579",
    "outputId": "49b77a4a-74e2-4f6f-cac7-2c5a55f2db51"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 2165.339570799884\n",
      "MSE: 8112909.576770614\n",
      "RMSE: 2848.3169726648425\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b129f3e4",
   "metadata": {
    "id": "b129f3e4"
   },
   "outputs": [],
   "source": [
    "# Saving model to disk\n",
    "pickle.dump(lm, open('model.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "308548f3",
   "metadata": {
    "id": "308548f3",
    "outputId": "c4541274-4e29-4ae9-ca3b-2cae758107ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The possible salary of this personnel is 58386.5844 $\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kasra Tehrani\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Loading model to compare the results\n",
    "model = pickle.load(open('model.pkl','rb'))\n",
    "print(\"The possible salary of this personnel is {:.4f} $\".format( float(model.predict([[3, 9, 6]]))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0437235-a9cc-4e90-9144-be5a78871f74",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "*Written by Kasra Tehrani*\n",
    "\n",
    "\n",
    "**https://www.linkedin.com/in/kasra-naderi-tehrani-a298b521b/ - https://github.com/kasra-python - kasra.n.tehrani@gmail.com**\n",
    "\n",
    "*Good luck!*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15379e80-ad33-4683-a692-611369ad46d2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}